Overview

Brought to you by YData

Dataset statistics

Number of variables 12
Number of observations 891
Missing cells 866
Missing cells (%) 8.1%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 83.7 KiB
Average record size in memory 96.1 B

Variable types

Numeric 5
Categorical 4
Text 3

Alerts

Sex is highly overall correlated with Survived High correlation
Survived is highly overall correlated with Sex High correlation
Age has 177 (19.9%) missing values Missing
Cabin has 687 (77.1%) missing values Missing
PassengerId is uniformly distributed Uniform
PassengerId has unique values Unique
Name has unique values Unique
SibSp has 608 (68.2%) zeros Zeros
Parch has 678 (76.1%) zeros Zeros
Fare has 15 (1.7%) zeros Zeros

Reproduction

Analysis started 2025-10-20 06:36:47.658731
Analysis finished 2025-10-20 06:36:50.794553
Duration 3.14 seconds
Software version ydata-profiling vv4.17.0
Download configuration config.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct 891
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 446
Minimum 1
Maximum 891
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 7.1 KiB
2025-10-20T12:06:50.939990 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 45.5
Q1 223.5
median 446
Q3 668.5
95-th percentile 846.5
Maximum 891
Range 890
Interquartile range (IQR) 445

Descriptive statistics

Standard deviation 257.35384
Coefficient of variation (CV) 0.57702655
Kurtosis -1.2
Mean 446
Median Absolute Deviation (MAD) 223
Skewness 0
Sum 397386
Variance 66231
Monotonicity Strictly increasing
2025-10-20T12:06:51.049122 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
891 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
Other values (881) 881
98.9%
Value Count Frequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
Value Count Frequency (%)
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%
882 1
0.1%

Survived
Categorical

High correlation 

Distinct 2
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
0
549 
1
342 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 891
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 1
3rd row 1
4th row 1
5th row 0

Common Values

Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Length

2025-10-20T12:06:51.123909 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T12:06:51.180383 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Most occurring characters

Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Pclass
Categorical

Distinct 3
Distinct (%) 0.3%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
3
491 
1
216 
2
184 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 891
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 3
2nd row 1
3rd row 3
4th row 1
5th row 3

Common Values

Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Length

2025-10-20T12:06:51.235404 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T12:06:51.285002 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring characters

Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Name
Text

Unique 

Distinct 891
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
2025-10-20T12:06:51.777528 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Length

Max length 82
Median length 52
Mean length 26.965208
Min length 12

Characters and Unicode

Total characters 24026
Distinct characters 60
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 891 ?
Unique (%) 100.0%

Sample

1st row Braund, Mr. Owen Harris
2nd row Cumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd row Heikkinen, Miss. Laina
4th row Futrelle, Mrs. Jacques Heath (Lily May Peel)
5th row Allen, Mr. William Henry
Value Count Frequency (%)
mr 521
 
14.4%
miss 182
 
5.0%
mrs 129
 
3.6%
william 64
 
1.8%
john 44
 
1.2%
master 40
 
1.1%
henry 35
 
1.0%
james 24
 
0.7%
george 24
 
0.7%
charles 23
 
0.6%
Other values (1515) 2538
70.0%
2025-10-20T12:06:52.370296 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 24026
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 24026
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 24026
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Sex
Categorical

High correlation 

Distinct 2
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
male
577 
female
314 

Length

Max length 6
Median length 4
Mean length 4.704826
Min length 4

Characters and Unicode

Total characters 4192
Distinct characters 5
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row male
2nd row female
3rd row female
4th row female
5th row male

Common Values

Value Count Frequency (%)
male 577
64.8%
female 314
35.2%

Length

2025-10-20T12:06:52.452432 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T12:06:52.516964 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
male 577
64.8%
female 314
35.2%

Most occurring characters

Value Count Frequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 4192
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 4192
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 4192
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Age
Real number (ℝ)

Missing 

Distinct 88
Distinct (%) 12.3%
Missing 177
Missing (%) 19.9%
Infinite 0
Infinite (%) 0.0%
Mean 29.699118
Minimum 0.42
Maximum 80
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 7.1 KiB
2025-10-20T12:06:52.603570 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0.42
5-th percentile 4
Q1 20.125
median 28
Q3 38
95-th percentile 56
Maximum 80
Range 79.58
Interquartile range (IQR) 17.875

Descriptive statistics

Standard deviation 14.526497
Coefficient of variation (CV) 0.48912219
Kurtosis 0.17827415
Mean 29.699118
Median Absolute Deviation (MAD) 9
Skewness 0.38910778
Sum 21205.17
Variance 211.01912
Monotonicity Not monotonic
2025-10-20T12:06:52.718212 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
24 30
 
3.4%
22 27
 
3.0%
18 26
 
2.9%
28 25
 
2.8%
30 25
 
2.8%
19 25
 
2.8%
21 24
 
2.7%
25 23
 
2.6%
36 22
 
2.5%
29 20
 
2.2%
Other values (78) 467
52.4%
(Missing) 177
 
19.9%
Value Count Frequency (%)
0.42 1
 
0.1%
0.67 1
 
0.1%
0.75 2
 
0.2%
0.83 2
 
0.2%
0.92 1
 
0.1%
1 7
0.8%
2 10
1.1%
3 6
0.7%
4 10
1.1%
5 4
 
0.4%
Value Count Frequency (%)
80 1
 
0.1%
74 1
 
0.1%
71 2
0.2%
70.5 1
 
0.1%
70 2
0.2%
66 1
 
0.1%
65 3
0.3%
64 2
0.2%
63 2
0.2%
62 4
0.4%

SibSp
Real number (ℝ)

Zeros 

Distinct 7
Distinct (%) 0.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.52300786
Minimum 0
Maximum 8
Zeros 608
Zeros (%) 68.2%
Negative 0
Negative (%) 0.0%
Memory size 7.1 KiB
2025-10-20T12:06:52.797513 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0
median 0
Q3 1
95-th percentile 3
Maximum 8
Range 8
Interquartile range (IQR) 1

Descriptive statistics

Standard deviation 1.1027434
Coefficient of variation (CV) 2.1084644
Kurtosis 17.88042
Mean 0.52300786
Median Absolute Deviation (MAD) 0
Skewness 3.6953517
Sum 466
Variance 1.2160431
Monotonicity Not monotonic
2025-10-20T12:06:52.855307 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
Value Count Frequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
4 18
 
2.0%
3 16
 
1.8%
8 7
 
0.8%
5 5
 
0.6%
Value Count Frequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
3 16
 
1.8%
4 18
 
2.0%
5 5
 
0.6%
8 7
 
0.8%
Value Count Frequency (%)
8 7
 
0.8%
5 5
 
0.6%
4 18
 
2.0%
3 16
 
1.8%
2 28
 
3.1%
1 209
 
23.5%
0 608
68.2%

Parch
Real number (ℝ)

Zeros 

Distinct 7
Distinct (%) 0.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.38159371
Minimum 0
Maximum 6
Zeros 678
Zeros (%) 76.1%
Negative 0
Negative (%) 0.0%
Memory size 7.1 KiB
2025-10-20T12:06:52.909548 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0
median 0
Q3 0
95-th percentile 2
Maximum 6
Range 6
Interquartile range (IQR) 0

Descriptive statistics

Standard deviation 0.80605722
Coefficient of variation (CV) 2.1123441
Kurtosis 9.7781252
Mean 0.38159371
Median Absolute Deviation (MAD) 0
Skewness 2.749117
Sum 340
Variance 0.64972824
Monotonicity Not monotonic
2025-10-20T12:06:52.967479 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
Value Count Frequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
5 5
 
0.6%
3 5
 
0.6%
4 4
 
0.4%
6 1
 
0.1%
Value Count Frequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
3 5
 
0.6%
4 4
 
0.4%
5 5
 
0.6%
6 1
 
0.1%
Value Count Frequency (%)
6 1
 
0.1%
5 5
 
0.6%
4 4
 
0.4%
3 5
 
0.6%
2 80
 
9.0%
1 118
 
13.2%
0 678
76.1%

Ticket
Text

Distinct 681
Distinct (%) 76.4%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
2025-10-20T12:06:53.432170 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Length

Max length 18
Median length 17
Mean length 6.7508418
Min length 3

Characters and Unicode

Total characters 6015
Distinct characters 35
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 547 ?
Unique (%) 61.4%

Sample

1st row A/5 21171
2nd row PC 17599
3rd row STON/O2. 3101282
4th row 113803
5th row 373450
Value Count Frequency (%)
pc 60
 
5.3%
c.a 27
 
2.4%
a/5 17
 
1.5%
ca 14
 
1.2%
2 12
 
1.1%
ston/o 12
 
1.1%
sc/paris 9
 
0.8%
w./c 9
 
0.8%
soton/o.q 8
 
0.7%
soton/oq 7
 
0.6%
Other values (709) 955
84.5%
2025-10-20T12:06:53.943426 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 6015
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 6015
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 6015
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Fare
Real number (ℝ)

Zeros 

Distinct 248
Distinct (%) 27.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 32.204208
Minimum 0
Maximum 512.3292
Zeros 15
Zeros (%) 1.7%
Negative 0
Negative (%) 0.0%
Memory size 7.1 KiB
2025-10-20T12:06:54.032584 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 7.225
Q1 7.9104
median 14.4542
Q3 31
95-th percentile 112.07915
Maximum 512.3292
Range 512.3292
Interquartile range (IQR) 23.0896

Descriptive statistics

Standard deviation 49.693429
Coefficient of variation (CV) 1.5430725
Kurtosis 33.398141
Mean 32.204208
Median Absolute Deviation (MAD) 6.9042
Skewness 4.7873165
Sum 28693.949
Variance 2469.4368
Monotonicity Not monotonic
2025-10-20T12:06:54.135823 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
8.05 43
 
4.8%
13 42
 
4.7%
7.8958 38
 
4.3%
7.75 34
 
3.8%
26 31
 
3.5%
10.5 24
 
2.7%
7.925 18
 
2.0%
7.775 16
 
1.8%
7.2292 15
 
1.7%
26.55 15
 
1.7%
Other values (238) 615
69.0%
Value Count Frequency (%)
0 15
1.7%
4.0125 1
 
0.1%
5 1
 
0.1%
6.2375 1
 
0.1%
6.4375 1
 
0.1%
6.45 1
 
0.1%
6.4958 2
 
0.2%
6.75 2
 
0.2%
6.8583 1
 
0.1%
6.95 1
 
0.1%
Value Count Frequency (%)
512.3292 3
0.3%
263 4
0.4%
262.375 2
0.2%
247.5208 2
0.2%
227.525 4
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 3
0.3%
164.8667 2
0.2%
153.4625 3
0.3%

Cabin
Text

Missing 

Distinct 147
Distinct (%) 72.1%
Missing 687
Missing (%) 77.1%
Memory size 7.1 KiB
2025-10-20T12:06:54.693231 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Length

Max length 15
Median length 3
Mean length 3.5882353
Min length 1

Characters and Unicode

Total characters 732
Distinct characters 19
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 101 ?
Unique (%) 49.5%

Sample

1st row C85
2nd row C123
3rd row E46
4th row G6
5th row C103
Value Count Frequency (%)
g6 4
 
1.7%
c23 4
 
1.7%
c25 4
 
1.7%
c27 4
 
1.7%
b96 4
 
1.7%
b98 4
 
1.7%
f 4
 
1.7%
d 3
 
1.3%
f2 3
 
1.3%
c22 3
 
1.3%
Other values (151) 201
84.5%
2025-10-20T12:06:55.317051 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
2 72
 
9.8%
C 71
 
9.7%
B 64
 
8.7%
1 61
 
8.3%
3 59
 
8.1%
6 51
 
7.0%
5 45
 
6.1%
4 37
 
5.1%
8 37
 
5.1%
D 34
 
4.6%
Other values (9) 201
27.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 732
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
2 72
 
9.8%
C 71
 
9.7%
B 64
 
8.7%
1 61
 
8.3%
3 59
 
8.1%
6 51
 
7.0%
5 45
 
6.1%
4 37
 
5.1%
8 37
 
5.1%
D 34
 
4.6%
Other values (9) 201
27.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 732
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
2 72
 
9.8%
C 71
 
9.7%
B 64
 
8.7%
1 61
 
8.3%
3 59
 
8.1%
6 51
 
7.0%
5 45
 
6.1%
4 37
 
5.1%
8 37
 
5.1%
D 34
 
4.6%
Other values (9) 201
27.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 732
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
2 72
 
9.8%
C 71
 
9.7%
B 64
 
8.7%
1 61
 
8.3%
3 59
 
8.1%
6 51
 
7.0%
5 45
 
6.1%
4 37
 
5.1%
8 37
 
5.1%
D 34
 
4.6%
Other values (9) 201
27.5%

Embarked
Categorical

Distinct 3
Distinct (%) 0.3%
Missing 2
Missing (%) 0.2%
Memory size 7.1 KiB
S
644 
C
168 
Q
77 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 889
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row S
2nd row C
3rd row S
4th row S
5th row S

Common Values

Value Count Frequency (%)
S 644
72.3%
C 168
 
18.9%
Q 77
 
8.6%
(Missing) 2
 
0.2%

Length

2025-10-20T12:06:55.384933 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T12:06:55.470443 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
s 644
72.4%
c 168
 
18.9%
q 77
 
8.7%

Most occurring characters

Value Count Frequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 889
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 889
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 889
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Interactions

2025-10-20T12:06:49.823682 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:48.148269 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:48.566367 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:48.950333 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:49.368572 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:49.915846 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:48.233605 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:48.644494 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:49.026796 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:49.440760 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:50.014827 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:48.328791 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:48.720340 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:49.104308 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:49.531813 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:50.111376 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:48.408708 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:48.799878 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:49.186672 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:49.633860 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:50.199909 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:48.491577 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:48.879407 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:49.289693 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-10-20T12:06:49.733447 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-20T12:06:55.576081 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Age Embarked Fare Parch PassengerId Pclass Sex SibSp Survived
Age 1.000 0.065 0.135 -0.254 0.041 0.269 0.099 -0.182 0.155
Embarked 0.065 1.000 0.196 0.052 0.000 0.260 0.113 0.092 0.166
Fare 0.135 0.196 1.000 0.410 -0.014 0.479 0.189 0.447 0.283
Parch -0.254 0.052 0.410 1.000 0.001 0.022 0.247 0.450 0.157
PassengerId 0.041 0.000 -0.014 0.001 1.000 0.032 0.066 -0.061 0.104
Pclass 0.269 0.260 0.479 0.022 0.032 1.000 0.130 0.148 0.337
Sex 0.099 0.113 0.189 0.247 0.066 0.130 1.000 0.206 0.540
SibSp -0.182 0.092 0.447 0.450 -0.061 0.148 0.206 1.000 0.187
Survived 0.155 0.166 0.283 0.157 0.104 0.337 0.540 0.187 1.000

Missing values

2025-10-20T12:06:50.399706 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-20T12:06:50.538016 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-20T12:06:50.736023 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S
5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q
6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S
7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 1 349909 21.0750 NaN S
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 2 347742 11.1333 NaN S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 NaN C
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
881 882 0 3 Markun, Mr. Johann male 33.0 0 0 349257 7.8958 NaN S
882 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22.0 0 0 7552 10.5167 NaN S
883 884 0 2 Banfield, Mr. Frederick James male 28.0 0 0 C.A./SOTON 34068 10.5000 NaN S
884 885 0 3 Sutehall, Mr. Henry Jr male 25.0 0 0 SOTON/OQ 392076 7.0500 NaN S
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q